Neural network based transient fuel control method

ABSTRACT

A system and method for use in a motor vehicles is disclosed for calculating a fuel multiplier during transient engine operation. The fuel multiplier modifies the amount of fuel released from a fuel actuator into an engine. The fuel control system uses neural network logic to establish the fuel multiplier. The neural network logic involves taking inputs from engine sensors, processing the inputs through an input layer, a hidden layer and an output layer resulting in a fuel multiplier.

This is a continuation-in-part of United States patent application No.08/387,544 filed Feb. 13, 1995.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to a methodology of transientfuel control for an internal combustion engine in a motor vehicle and,more specifically, to a neural network based transient fuel controlmethod for an internal combustion engine in a motor vehicle.

2. Description of the Related Art

A motor vehicle is typically powered by an internal combustion enginewhich generally operates in two modes, steady state and transient. Asteady state mode could be characterized by maintaining the vehicle at aconstant engine speed and load, whereby the power demands on the enginedo not fluctuate. In contrast, a transient mode could be characterizedby driving the vehicle under varying condition,s such as accelerating,decelerating, or climbing a grade, whereby the power demands arecontinuously changing.

Precise metering of the amount of fuel and air delivered to the engineis necessary to achieve the desired combustion as well as acceptablevehicle emissions. It is more difficult to maintain this mixture, knownas the air/fuel ratio, when the vehicle is operating in a transientmode. For example, when the vehicle is accelerating, fuel demandsincrease to meet increased power needs. Conversely, when the vehicle isdecelerating, fuel demands decrease to meet decreased powerrequirements. At steady state, fuel demands remain constant.

Traditionally, an engine controller receives inputs from a variety ofsensors providing information regarding vehicle operating conditionssuch as engine speed, throttle position, and spark advance. Thecontroller processes the information, determines the requisite amount ofair and fuel, and relays this information to a fuel actuator (e.g., afuel injector), which adjusts the fuel flow rate to provide the preciseamount of fuel to the engine. One method of determining the amount offuel is through the use of look-up tables within the controllercontaining information regarding the amount of fuel required duringcertain vehicle operating conditions, including transient and steadystate modes. The information contained within the tables isexperimentally derived from a combination of vehicle testing andengineering experience. This process can be time consuming and costly.

SUMMARY OF THE INVENTION

It is, therefore, one object of the present invention to provide aneural network based system and method for controlling fuel delivered toan internal combustion engine during transient driving conditions.

It is another object of the present invention to provide a fuel controlsystem utilizing neural network technology to establish a fuelmultiplier during transient modes.

It is yet another object of the present invention to provide a moreprecise method of determining fuel delivery to an internal combustionengine during transient modes.

It is a further object of the present invention to provide an improvisedmethod for fuel control for a new motor vehicle.

To achieve the foregoing objects, the present invention is a neuralnetwork based system and method for controlling fuel flow in a vehiclefuel control system operating in a transient mode. The vehicle fuelcontrol system comprises a plurality of inputs for detecting vehicleconditions, including engine speed, manifold absolute pressure, throttleposition, AIS motor position, oxygen, and spark advance position, and aneural network based controller for calculating a fuel multiplier basedon the inputs, such that the fuel multiplier controls fuel flow in thevehicle fuel control system. This neural network based controllercomprises an input node for each of the input sensors, for normalizingeach of the inputs and for multiplying each of the inputs by an inputweight. Two hidden nodes receive and sum the output from each of theinput nodes into an intermediate output, and then a hyperbolic tangentfunction for each hidden node receives this intermediate output andtransmits a hidden layer output. Next, an output node receives thehidden layer output from each of the hidden nodes, multiplies each ofthese hidden layer outputs by an output weight resulting in a weightedoutput, and sums the weighted output into a summed output, before anoutput hyperbolic tangent function receives this summed output,transmits an output layer output and denormalizes the output layeroutput into the fuel multiplier.

One advantage of the present invention is that a neural network basedtransient fuel control system and method is provided for an internalcombustion engine. Another advantage of the present invention is thatthe design method optimized the inputs and network topology used toimplement the neural network fuel control system. Yet another advantageof the present invention is that the method improves the developmentcycle for a new vehicle since testing time is decreased and there isincreased commonality between different engine types.

Other objects, features and advantages of the present invention will bereadily appreciated as the same becomes better understood after readingthe subsequent description taken in conjunction with the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system block diagram of a vehicle fuel control system.

FIG. 2 is a system block diagram of a typical vehicle fuel controlstrategy using a look-up table approach.

FIGS. 3, 4 and 5 are flowcharts of the design process for implementing aneural network based vehicle fuel control system of the presentinvention.

FIG. 6 is a system block diagram of neural network logic for the vehiclefuel control system of the present invention.

FIGS. 7A and 7B are flowcharts of the neural network methodology for thevehicle fuel control system of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENT(S)

FIG. 1 illustrates a vehicle fuel control system 10 is shown for a motorvehicle such as an automotive vehicle (not shown). The system 10includes an engine controller 12 containing hardware and softwarenecessary to control air/fuel ratio, spark advance, exhaust gasrecirculation and other engine control tasks. The engine controller 12receives information from a number of sensors 14 which may include, butare not limited to oxygen, throttle position, manifold absolute pressure(MAP), and engine speed (RPM), as well as computes additionalinformation, such as spark advance position and automatic idle speed(AIS) motor position, from some of this detected information. Based onsome of this information, the engine controller 12 then sends a signalto a fuel actuator 16 varying the fuel actuator's pulsewidth to adjustthe delivery of fuel from the fuel actuator 16 into an internalcombustion engine 18 in an amount necessary to achieve a predeterminedair/fuel ratio.

Referring to FIG. 2, a typical vehicle fuel control strategy is shownwithin the engine controller 12. The engine controller 12 receivesinputs 15 from the sensors 14, determines whether the engine 18 isoperating in a steady state mode 20 or a transient mode 22, andcalculates a fuel multiplier 24. Typically, the fuel multiplier 24 isbased on three inputs (e.g., manifold absolute pressure, throttleposition and AIS motor position) and derived from a look-up table storedin memory of the engine controller 12 as is well known in the art. Thefuel multiplier 24 is then used to calculate the final fuel value, whichthen modifies the fuel flow through the fuel actuator 16.

According to the present invention, a method for designing a neuralnetwork based fuel control system for controlling fuel flow to an engineof a motor vehicle is shown in FIGS. 3-5. In FIG. 3, the design processbegins by equipping 26A a test vehicle with a non-productioninstrumentation (i.e., a linear exhaust oxygen sensor) for calibratingthe desired neural network output. As will be apparent to one skilled inthe art, the test vehicle will be comprised of numerous production andnon-production instrumentation depending upon the design project. Toestablish initial conditions 26B, current transient fuel controlalgorithms (i.e., look-up table approach) are disabled, but steady statefuel control algorithms remain enabled.

Input data is captured/acquired 26C by using a combination of sensors,including (but not limited to) an engine speed sensor, a manifoldabsolute pressure sensor, a throttle position sensor, an oxygen sensor,charge temperature sensor, and an engine coolant temperature sensor.Spark advance position and AIS motor position are additional inputs thatare computed by the controller. Each set of input data represents aparticular state (or condition) of the engine. This input data may beacquired during Federal emission tests that include both city andhighway driving cycles, and thus represent transient conditions of thevehicle's operation. Signal processing may be performed on input sensordata to create calculated input data (e.g., a first or second derivativeof engine speed, manifold absolute pressure, and throttle position) andthe network's output (or its derivative) may also be feedback as inputdata.

Next, the desired fuel multiplier is calculated 26D for each set ofinput data. The current fuel pulsewidth multiplier necessary tocompensate the steady state fuel pulsewidth at the current engine stateis calculated based on the actual fuel/air ratio resulting from thecurrent steady state fuel pulsewidth as follows: ##EQU1##

where (tf)=Flow Delay Time (RPM, Sensor Response Time & Location)

if lean condition exists, then fuel multiplier(t)>1

if stoichiometric condition exists, then multiplier(t)=1

if rich condition exists, then fuel multiplier(t)<1

The actual fuel/air ratio is measured at a time (tf) after the currentcombustion event by placing a linear oxygen sensor downstream (i.e., inthe exhaust system) from the engine. Therefore, the desired fuelmultiplier is determined by mapping a previous (input) engine state tothe actual (measured) fuel/air ratio. This flow delay time (tf) isdetermined based on engine speed, oxygen sensor response time andlocation (i.e., distance) of oxygen sensor from the engine as is wellknown in the art.

Initially, a basic neural network topology (i.e., single layer,feedforward network) is implemented in FIG. 4. Supervised training 26Eusing at least a portion of the acquired and calculated input data andcorresponding desired fuel multiplier is employed to configure thisfirst neural network. Training is an experimental methodology forlearning what constant values should be used and takes place during thedevelopment phase of designing a new motor vehicle. Backpropagation isthe preferred training method, although other training methods may alsobe used in establishing network weights. The remainder of the input dataand corresponding desired fuel multipliers are used to test 26F theaccuracy of this first neural network. During this initial training andtesting, each and every acquired and calculated input serves as an inputinto this first neural network.

Network inputs are optimized 26G based on the weighting determinationfrom the neural network training. Weighting values fall within a rangearound 0 (e.g., between -1.5 and 1.5), such that the farther a weight isfrom 0 the more that input correlates to (or contains information about)the desired fuel multiplier. In general, inputs with a distinctivegrouping or cluster of weighting values were identified as the preferredinputs from the particular set of inputs being tested. Inputs withweighting values within an order of magnitude of each other (based onpercentage difference in their absolute weighting values) were groupedtogether; whereas inputs outside this grouping (e.g., with weightingvalues close to 0) were not chosen. This input optimization techniquewas used with a variety of network topologies to eliminate networkdependencies. The identified cluster of inputs are then used toreconfigure and retest the current (or variation of the) networktopology. Engine speed (RPM), manifold absolute pressure, throttleposition, AIS motor position, oxygen sensor, and spark advance positionwere identified as the optimal inputs for controlling transient fuelflow to an engine.

To refine the design process, additional topologies are implemented, andthen the steps of training, testing and input optimization are repeatedfor each topology. Variations in the network topologies that may beimplemented, include adding hidden layer(s), varying the number of nodesin each hidden layer, using different activation functions (i.e.,sigmoidal function, constant value, etc.), and employing a recurrentnetwork approach. An optimal network topology (as seen in FIG. 6) wasselected 26H based on neural network convergence rates and the accuracyof network output in relation to desired fuel multipliers.

Referring to FIG. 5, once an optimal network topology has been selectedfor the optimal grouping of inputs, a final neural network isimplemented 26I in an embedded controller using modular programmingstructure. Although off-line training was employed to reduce theimplementation requirements, one skilled in the art will recognize thaton-line or real-time training could be used to allow for adaptivelearning of new engine operating conditions. Finally, vehicle testing26J is conducted using this neural network based fuel control system toevaluate vehicle drivability and compliance with Federal emissionstandards.

Through this iterative design approach, a preferred embodiment of aneural network based fuel control system is shown in FIG. 6. The neuralnetwork based fuel control system and method uses a neural network logicsystem, generally shown at 28, to provide logic in an organized mannerand for manipulating data to establish the fuel multiplier 24 when theengine 18 is operating in a transient mode. This neural network logicsystem 28 is programmed into the engine controller 12.

In the neural network logic system 28, an input layer 29 receives inputs15 from the sensors 14. Engine speed (RPM), manifold absolute pressure,throttle position, AIS motor position, oxygen sensor, and spark advanceposition are the preferred inputs as identified through the optimizationprocess. These inputs 15 are each normalized, and then multiplied by aninput layer weight 30 (W1 through W6) to obtain an input layer output32. In this particular example, there are two input layer weights 30 foreach input 15, corresponding to a number of hidden layer nodes 36.However, there could be more or less, depending on design criteria. Eachinput layer weight 30 is a constant value derived from training.

The input layer output 32 is then processed in a hidden layer 34. Thisprocess includes summing together the input layer output 32 within eachhidden layer node 36, resulting in an intermediate output 38 for eachhidden layer node 36. In this particular example, there are two (2)hidden layer nodes 36, although there could be more or fewer nodesdepending upon the need. The process also includes a hyperbolic tangent(TanH) transfer function 40 whereby the hyperbolic tangent of eachintermediate output 38 from each hidden layer node 36 is taken resultingin an hidden layer output 42.

The hidden layer output 42 is then processed in an output layer 48 bymultiplying the hidden layer output 42 by a corresponding output layerweight 44 (W7, W8), and a resulting weighted output 46 is summedtogether in an output layer node 50. A resulting summed output 52 fromthe output layer mode 50 is then put through an output hyperbolictangent transfer function 54 whereby the hyperbolic tangent is taken.The resulting value is an output layer output 64, which is thendenormalized to become the fuel multiplier 24.

Concurrently, a bias layer 56, which provides a reference value for thenodes, feeds the nodes in the output layer 48 and the hidden layer 34.An arbitrarily chosen bias value 58, in this example one (1), ismultiplied by a bias weight 60 (W9, W10, W11) resulting in an bias layeroutput 62. In another embodiment, there may be a unique bias weight 60corresponding to each node, which is learned using a method such astraining. The bias layer output 62 then becomes an input to thecorresponding hidden layer node 50.

Referring for FIGS. 7A and 7B, a flowchart of the neural network basedmethod, according to the present invention, is shown. In FIG. 7A, themethodology begins or starts in block 100 an occurs after the motorvehicle is keyed ON. The methodology advances to block 102 where initialconditions are established such as input layer weight 30, output layerweight 44 and bias weight 60. The methodology progresses to block 104where further initial conditions are established, such as defining theinput, output and transfer function scaling by the hyperbolic tangenttransfer functions. After block 104, the rest of the methodology occurson a periodic basis. It should be appreciated that the methodology canbe trained to recognize when the vehicle is operating in a steady statemode, and output a fuel multiplier equal to a constant value such as 1.The methodology then advances to block 106 and reads engine sensor input15 from a sensor 14. The methodology progresses to block 108, where thesensor input 15 is normalized for a data point to be within the range-1.0 to +1.0. A typical normalization calculation to obtain a normalizedinput is as follows:

    Scale=(high-low)/(max-min)

    Offset=[(max)(low)-(min)(high)]/(max-min)

    Normalization=(input)(scale)+offset

The methodology advances to block 110 where the normalized input isstored in random access memory (RAM) in the engine controller 12 forfuture use.

After block 110, the methodology advances to block 112 where the neuralnetwork processing begins and continues to block 114. In block 114, thenormalized input is multiplied by an input layer weight 30 resulting inan input layer output 32. The methodology then advances to block 116,the hidden layer 34, where the input layer output 32 is summed together.The methodology advances to block 118 where the bias layer output 62 isadded to the input layer output 32 resulting in an intermediate output38 for each hidden layer node 36. The methodology advances to block 120where the intermediate output 38 is put through a hyperbolic tangenttransfer function resulting in a hidden layer output 42. The methodologyadvances to block 122 and this value is stored in the engine controller12 for future processing.

After block 122, the methodology advances to block 124 in Figure 7B. Inblock 124, the output layer weight 44 is multiplied by the hidden layeroutput 42 resulting in a weighted output 46 for each. The methodologythen advances to block 126 and the weighted output 46 is summedtogether. The methodology advances to block 128 where the bias layeroutput 62 is added to the weighted output 46 resulting in a summedoutput 52.

After block 128, the methodology advances to block 130 where the summedoutput 52 is put through an output hyperbolic transfer function 54 bytaking the hyperbolic tangent of the summed output 52 resulting in anoutput layer output 64. The methodology advances to block 132 and theoutput layer 64 is stored for future processing. The methodology thenadvances to block 134 and the output layer 64 is denormalized for use asthe fuel multiplier 24. The denormalization calculation is as follows:

    Scale=(high-low)/(max-min)

    Offset=[(max)(low)-(min)(high)]/(max-min)

    Denormalization=(output-offset)/(scale)

The methodology continues to block 136 where the fuel multiplier 24 isstored in the engine controller 12 until needed. The methodology thenreturns to block 106.

When the fuel multiplier 24 is called for by the engine controller 12,the methodology calculates the final fuel value in block 138 as:

    Final Fuel Value=(Steady State Fuel Control)*(Fuel Multiplier)

The methodology then advances to block 140 where the final fuel value isstored in the engine controller 12 until needed.

The present invention has been described in an illustrative manner. Itis to be understood that the terminology which has been used is intendedto be in the nature of words of description rather than of limitation.Many modifications and variations of the present invention are possiblein light of the above teachings. Therefore, within the scope of theappended claims, the present invention may be practiced otherwise thanas specifically described.

What is claimed is:
 1. A vehicle fuel control system for controlling afuel flow to an engine of a motor vehicle operating in a transient mode,comprising:a plurality of input sensors for detecting an engine state,including a speed sensor, a manifold absolute pressure sensor, athrottle position sensor, an AIS motor position sensor, an oxygensensor, and a spark advance position sensor; a neural network forcalculating a fuel multiplier based on input from each of said pluralityof sensors, whereby the fuel multiplier controls fuel flow in thevehicle fuel control system; and a fuel actuator for receiving the fuelmultiplier and adjusting the fuel flow to the engine of the vehicle. 2.The vehicle fuel control system of claim 1 further comprising a meansfor training said neural network using input data from each of saidplurality of sensors and a desired fuel multiplier, where the desiredfuel multiplier is determined by measuring an fuel/air ratio whichcorresponds to the input data from each of said plurality of inputsensors.
 3. The vehicle fuel control system of claim 1 wherein saidneural network includes:an input node for each of said input sensors fornormalizing each of said inputs and multiplying each of said inputs byan input weight; two hidden nodes receiving and summing the output fromeach of said input nodes into an intermediate output; a hyperbolictangent function for each hidden node receiving said intermediate outputand transmitting a hidden layer output; an output node receiving thehidden layer output from each of said hidden nodes, multiplying each ofsaid hidden layer outputs by an output weight resulting in a weightedoutput, and summing said weighted output into a summed output; and aoutput hyperbolic tangent function receiving said summed output,transmitting an output layer output and denormalizing said output layeroutput into said fuel multiplier.
 4. The vehicle fuel control system ofclaim 3 wherein said neural network means further comprises a bias layerfor providing a reference value to each of the two hidden nodes and theoutput node.
 5. A vehicle fuel control method for controlling fuel flowto an engine of a motor vehicle operating in a transient mode,comprising the steps of:determining engine speed, manifold absolutepressure, throttle position, AIS motor position, oxygen, and sparkadvance position using a plurality of sensors, thereby determining anengine state; determining a fuel multiplier using a neural networkhaving as inputs engine speed, manifold absolute pressure, throttleposition, AIS motor position, oxygen, and spark advance position; andusing the fuel multiplier to control fuel flow in the vehicle fuelcontrol system.
 6. The vehicle fuel control method of claim 5 whereinthe step of using the fuel multiplier further comprises adjusting a fuelactuator to control the fuel flow into the engine of the vehicle.
 7. Thevehicle fuel control method of claim 5 further comprising the step oftraining said neural network using input data from each of saidplurality of sensors and a desired fuel multiplier, where the desiredfuel multiplier is determined by measuring an actual fuel/air ratiowhich corresponds to the input data from each of said plurality of inputsensors.
 8. The vehicle fuel control method of claim 5 furthercomprising the step of normalizing each input from said plurality ofsensors prior to being input into said neural network.
 9. The vehiclefuel control method of claim 5 wherein said neural network includes:aninput node for each of said input sensors for multiplying each of saidinputs by an input weight; two hidden nodes receiving and summing theoutput from each of said input nodes into an intermediate output; ahyperbolic tangent function for each hidden node receiving saidintermediate output and transmitting a hidden layer output; an outputnode receiving the hidden layer output from each of said hidden nodes,multiplying each of said hidden layer outputs by an output weightresulting in a weighted output, and summing said weighted output into asummed output; and a output hyperbolic tangent function receiving saidsummed output, and transmitting an output layer output.
 10. The vehiclefuel control method of claim 9 further comprising the step ofdenormalizing said output layer output into said fuel multiplier, andmultiplying said fuel multiplier by a steady state fuel control valueresulting in a final fuel value.
 11. The vehicle fuel control method ofclaim 9 wherein said neural network further comprises a bias layer forproviding a reference value to each of the two hidden nodes and theoutput node.